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feat(seo): add basic SEO files for Google Search Console (#528)
## Summary - Add `robots.txt` with sitemap reference - Add `sitemap.xml` with main page URL - Add Open Graph and Twitter Card meta tags to `index.html` - Add `og-image.png` (1200x630px) for social media previews - Update meta description to match website tagline ## Files Added/Changed | File | Change | |------|--------| | `app/public/robots.txt` | New - crawl rules + sitemap reference | | `app/public/sitemap.xml` | New - main page URL | | `app/index.html` | Updated - OG, Twitter Card, canonical tags | | `app/public/og-image.png` | New - social media preview image | ## After Deployment 1. Submit sitemap in Google Search Console: `https://pyplots.ai/sitemap.xml` 2. Request URL inspection for `https://pyplots.ai/` 3. Test OG tags at https://www.opengraph.xyz/
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‎app/index.html‎

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<meta charset="UTF-8" />
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<link rel="icon" type="image/svg+xml" href="/favicon.svg" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<meta name="description" content="AI-powered Python plotting examples that work with YOUR data" />
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<title>pyplots - AI-Powered Plotting Examples</title>
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<meta name="description" content="library-agnostic, ai-powered python plotting examples. automatically generated, tested, and maintained." />
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<title>pyplots.ai</title>
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<!-- Canonical -->
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<link rel="canonical" href="https://pyplots.ai/" />
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<!-- Open Graph -->
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<meta property="og:type" content="website" />
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<meta property="og:url" content="https://pyplots.ai/" />
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<meta property="og:title" content="pyplots.ai" />
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<meta property="og:description" content="library-agnostic, ai-powered python plotting examples. automatically generated, tested, and maintained." />
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<meta property="og:image" content="https://pyplots.ai/og-image.png" />
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<meta property="og:site_name" content="pyplots.ai" />
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<!-- Twitter Card -->
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<meta name="twitter:card" content="summary_large_image" />
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<meta name="twitter:title" content="pyplots.ai" />
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<meta name="twitter:description" content="library-agnostic, ai-powered python plotting examples. automatically generated, tested, and maintained." />
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<meta name="twitter:image" content="https://pyplots.ai/og-image.png" />
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<link rel="preconnect" href="https://fonts.googleapis.com">
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<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
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<link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@400;600;700;800&display=swap" rel="stylesheet">

‎app/public/og-image.png‎

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‎app/public/robots.txt‎

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User-agent: *
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Allow: /
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Sitemap: https://pyplots.ai/sitemap.xml

‎app/public/sitemap.xml‎

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<?xml version="1.0" encoding="UTF-8"?>
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<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
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<url>
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<loc>https://pyplots.ai/</loc>
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<lastmod>2025-12-07</lastmod>
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<changefreq>weekly</changefreq>
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<priority>1.0</priority>
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</url>
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</urlset>

‎plots/bokeh/scatter/scatter-color-groups/default.py‎

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np.random.seed(42)
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n_per_group = 50
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data = pd.DataFrame({
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"sepal_length": np.concatenate([
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np.random.normal(5.0, 0.35, n_per_group),
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np.random.normal(5.9, 0.50, n_per_group),
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np.random.normal(6.6, 0.60, n_per_group),
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]),
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"sepal_width": np.concatenate([
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np.random.normal(3.4, 0.38, n_per_group),
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np.random.normal(2.8, 0.30, n_per_group),
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np.random.normal(3.0, 0.30, n_per_group),
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]),
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"species": ["setosa"] * n_per_group + ["versicolor"] * n_per_group + ["virginica"] * n_per_group,
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})
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data = pd.DataFrame(
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{
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"sepal_length": np.concatenate(
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[
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np.random.normal(5.0, 0.35, n_per_group),
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np.random.normal(5.9, 0.50, n_per_group),
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np.random.normal(6.6, 0.60, n_per_group),
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]
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),
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"sepal_width": np.concatenate(
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[
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np.random.normal(3.4, 0.38, n_per_group),
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np.random.normal(2.8, 0.30, n_per_group),
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np.random.normal(3.0, 0.30, n_per_group),
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]
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),
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"species": ["setosa"] * n_per_group + ["versicolor"] * n_per_group + ["virginica"] * n_per_group,
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}
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)
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# Color palette (from style guide)
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colors = ["#306998", "#FFD43B", "#DC2626", "#059669", "#8B5CF6", "#F97316"]

‎plots/matplotlib/scatter/scatter-color-groups/default.py‎

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np.random.seed(42)
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n_per_group = 50
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data = pd.DataFrame({
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"sepal_length": np.concatenate([
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np.random.normal(5.0, 0.35, n_per_group),
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np.random.normal(5.9, 0.50, n_per_group),
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np.random.normal(6.6, 0.60, n_per_group),
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]),
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"sepal_width": np.concatenate([
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np.random.normal(3.4, 0.38, n_per_group),
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np.random.normal(2.8, 0.30, n_per_group),
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np.random.normal(3.0, 0.30, n_per_group),
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]),
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"species": ["setosa"] * n_per_group + ["versicolor"] * n_per_group + ["virginica"] * n_per_group,
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})
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data = pd.DataFrame(
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{
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"sepal_length": np.concatenate(
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[
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np.random.normal(5.0, 0.35, n_per_group),
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np.random.normal(5.9, 0.50, n_per_group),
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np.random.normal(6.6, 0.60, n_per_group),
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]
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),
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"sepal_width": np.concatenate(
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[
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np.random.normal(3.4, 0.38, n_per_group),
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np.random.normal(2.8, 0.30, n_per_group),
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np.random.normal(3.0, 0.30, n_per_group),
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]
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),
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"species": ["setosa"] * n_per_group + ["versicolor"] * n_per_group + ["virginica"] * n_per_group,
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}
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)
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# Color palette (colorblind safe from style guide)
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colors = ["#306998", "#FFD43B", "#DC2626"]

‎plots/plotnine/point/scatter-color-groups/default.py‎

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np.random.seed(42)
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n_per_group = 50
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data = pd.DataFrame({
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"sepal_length": np.concatenate([
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np.random.normal(5.0, 0.35, n_per_group),
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np.random.normal(5.9, 0.50, n_per_group),
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np.random.normal(6.6, 0.60, n_per_group),
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]),
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"sepal_width": np.concatenate([
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np.random.normal(3.4, 0.38, n_per_group),
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np.random.normal(2.8, 0.30, n_per_group),
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np.random.normal(3.0, 0.30, n_per_group),
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]),
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"species": ["setosa"] * n_per_group + ["versicolor"] * n_per_group + ["virginica"] * n_per_group,
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})
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data = pd.DataFrame(
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{
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"sepal_length": np.concatenate(
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[
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np.random.normal(5.0, 0.35, n_per_group),
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np.random.normal(5.9, 0.50, n_per_group),
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np.random.normal(6.6, 0.60, n_per_group),
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]
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),
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"sepal_width": np.concatenate(
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[
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np.random.normal(3.4, 0.38, n_per_group),
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np.random.normal(2.8, 0.30, n_per_group),
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np.random.normal(3.0, 0.30, n_per_group),
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]
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),
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"species": ["setosa"] * n_per_group + ["versicolor"] * n_per_group + ["virginica"] * n_per_group,
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}
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)
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# Color palette (from style guide)
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colors = ["#306998", "#FFD43B", "#DC2626"]

‎tests/unit/prompts/test_prompts.py‎

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"plotnine.md",
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"pygal.md",
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"highcharts.md",
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"letsplot.md",
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]
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return (PROMPTS_DIR / "quality-criteria.md").read_text()
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def test_plot_generator_has_required_sections(self, plot_generator_content: str) -> None:
84-
"""Plot generator should have Role, Task, Rules sections."""
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required_sections = ["## Role", "## Task", "## Rules", "## Output"]
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"""Plot generator should have Role, Task, Output sections."""
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# Core sections at level 2
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required_sections = ["## Role", "## Task", "## Output"]
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for section in required_sections:
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assert section in plot_generator_content, f"Missing section: {section}"
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# Rules can be at level 2 or 3 (### Rules under ## Output)
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assert "Rules" in plot_generator_content, "Missing Rules section"
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def test_plot_generator_has_code_template(self, plot_generator_content: str) -> None:
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"""Plot generator should include a code template."""
9195
assert "```python" in plot_generator_content, "Missing Python code template"
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assert "def create_plot" in plot_generator_content, "Missing create_plot function template"
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# KISS style: simple scripts with comments, not functions
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assert "# Create plot" in plot_generator_content or "plt.savefig" in plot_generator_content, (
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"Missing plot creation example"
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)
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def test_quality_criteria_has_scoring_section(self, quality_criteria_content: str) -> None:
95102
"""Quality criteria should have scoring information."""
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content = (LIBRARY_PROMPTS_DIR / filename).read_text()
108115
library_name = filename.replace(".md", "")
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110-
# Check for header
111-
assert f"# {library_name}" in content.lower(), f"Missing header for {library_name}"
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# Check for header (normalize by removing hyphens for comparison)
118+
content_normalized = content.lower().replace("-", "")
119+
assert f"# {library_name}" in content_normalized, f"Missing header for {library_name}"
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113121
# Check for import section
114122
assert "## Import" in content or "import" in content.lower(), f"Missing import section in {filename}"
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117125
assert "```python" in content, f"Missing Python code examples in {filename}"
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119127
@pytest.mark.parametrize("filename", EXPECTED_LIBRARY_PROMPTS)
120-
def test_library_prompt_has_return_type(self, filename: str) -> None:
121-
"""Each library prompt should specify return type."""
128+
def test_library_prompt_has_save_section(self, filename: str) -> None:
129+
"""Each library prompt should show how to save the plot."""
122130
content = (LIBRARY_PROMPTS_DIR / filename).read_text()
123-
# Either explicit return type section or type hint in code
124-
has_return_type = "## Return Type" in content or "-> " in content
125-
assert has_return_type, f"Missing return type specification in {filename}"
131+
# KISS style: prompts show how to save, not function return types
132+
save_patterns = ["## Save", "savefig", "save(", "write_image", "save_screenshot", "export_png"]
133+
has_save_info = any(pattern in content for pattern in save_patterns)
134+
assert has_save_info, f"Missing save/output section in {filename}"
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128137
class TestNoPlaceholders:

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